CN113851185B - 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 - Google Patents
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Abstract
本发明涉及医学分子生物学技术领域,特别涉及一种用于非小细胞肺癌患者免疫治疗的预后评估方法,包括通过测序检测基因变异、创建决策树模型并筛选出13个最优特征基因、构建预测模型对非小细胞肺癌TMB‑H患者进行预后风险评分,从而预测出病人的预后情况。与现有技术相比,本发明提供的一种用于非小细胞肺癌TMB‑H患者免疫治疗的预后评估方法,通过对非小细胞肺癌TMB‑H患者样本进行测序,基于CART算法,构建包含13个最优的特征基因的预测模型,根据预测模型对非小细胞肺癌TMB‑H患者进行预后风险评分,将TMB‑H人群分为预后良好组与预后较差组,准确率高达0.85。
Description
技术领域
本发明涉及医学分子生物学技术领域,特别涉及一种用于非小细胞肺癌患者免疫治疗的预后评估方法。
背景技术
免疫治疗药物通过抑制肿瘤细胞的免疫逃逸,调动患者自身免疫***功能消除肿瘤。目前免疫治疗已在多种晚期实体肿瘤治疗中取得了突破性进展,尤其是可有效延长患者总生存期(Overall survival,OS),且不良反应可控。但由于缺乏合适的临床分子标志物,PD-1/PD-L1免疫治疗药物的受益人群只有20%-30%。TMB的精确测量可以预测免疫检查点抑制剂的疗效,使癌症患者有机会获得更加精准的治疗。既往临床研究及转化研究显示,基于组织检测肿瘤突变负荷(Tumor Mutation Burden,TMB)状态与客观缓解率(Objective response rate,ORR)、无进展生存期(Progression-free survival,PFS)以及OS相关,因此被认为是指导免疫治疗的重要标记物。一般认为TMB值较高时(TMB-H),免疫检验点抑制剂效果较好。但有大量报道指出当使用TMB作为指标进行免疫治疗获益人群划分时,存在特异度较差情况,具体表现为TMB-H非小细胞肺癌人群中存在免疫治疗后OS较短患者。亟待其他标志物辅助TMB进行人群的二次划分,提高TMB-H免疫治疗获益人群分类效果。
发明内容
针对以上述背景技术的不足,本发明提供一种用于非小细胞肺癌患者免疫治疗的预后评估方法。
本发明采用的技术方案如下:一种用于非小细胞肺癌患者免疫治疗的预后评估方法,关键在于:包括以下步骤:
S1. 对非小细胞肺癌患者进行基因靶向测序,获取基因变异情况;
S2. 将患者基因突变列表与预后情况输入监督学习决策树模型中,基于CART算法,建立分类模型,创建决策树模型,筛选出13个最优特征基因;
S3. 构建包含13个最优的特征基因的预测模型式I,用于对非小细胞肺癌TMB-H患者进行预后风险评分,从而预测出病人的预后情况;
优选的,所述S1具体为:采集非小细胞肺癌TMB-H患者样本进行靶向深度测序,检测panel基因变异情况。
优选的,所述S2具体为:将检测到的基因变异做为训练特征,以总生存期OS<12 月以及总生存期OS>12月作为机器学习的分类结果,使用监督学习决策树模型CART算法,通过特征选择、剪枝、交叉验证、模型持久化四个步骤,创建决策树模型。
优选的,所述特征选择具体为:使用公式II所示的基尼系数作为衡量标准,来计算通过不同的特征进行分支选择后的分类情况,找出最好的分类特征作为根节点,并以此类推,直到建树结束,创建复杂树模型;
其中K表示类别, p为第k个类别的概率,Gini(p)越小,则纯度越高,特征越好。
优选的,所述剪枝具体为:通过公式III所示的极小化决策树整体的损失函数来实现,对所述复杂树模型自下往上的对非叶子结点进行考回缩,若将该结点对应的子树替换为叶结点能带来泛化性能提升,则进行剪枝,即将父结点变为新的叶子结点,从而对将生成的树进行简化,以避免过拟合情况的发生;
其中,C(T)表示模型与训练数据的拟合程度,|T|表示模型复杂度。
优选的,所述交叉验证具体为:通过公式IV所示的准确度公式,将原始数据集随机地分成5份,每次将其中4份作为训练集来训练模型,剩余1份作为验证集验证模型,得到验证集的准确度,轮流进行5次,直到所有的数据都被验证了一次且仅被验证一次,循环计算每组模型准确度得分平均值,取平均值最高为最优模型,从而评价模型的泛化能力,从而进行模型及参数选择;
其中,Accuracy 代表准确率,TP:被正确分类的正例样本个数,TN:被正确分类的负例样本个数,FP:被错误分类的负例样本个数,FN:被错误分类的正例样本个数。
优选的,所述13个最优特征基因为SMARCB1、TSC2、 BAP1、SDHB、RIT1、ESR1、SOCS1、SH2B3、IDH2、MET、 BRIP1、NTRK3、FGFR4。
优选的,将TMB-H患者的样本进行靶向深度测序,检测基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后较好组与较差组。
有益效果:与现有技术相比,本发明提供的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,通过采集非小细胞肺癌TMB-H患者样本,基于CART算法,构建包含13个最优的特征基因的预测模型,根据预测模型对非小细胞肺癌TMB-H患者进行预后风险评分,将TMB-H人群分为预后较好组与较差组,准确率高达0.85。
附图说明
图1为本发明预测模型的单因素cox回归分析图;
图2为13个基因突变与整体生存相关性示意图;
图3为不同基因构建模型对TMB-H人群的DCR示意图。
具体实施方式
为使本领域技术人员更好的理解本发明的技术方案,下面结合附图和具体实施方式对本发明作详细说明。
实施例1
随机选择TMB-H组的155个患者,对各患者样本进行基因靶向测序,将各患者基因突变列表与预后情况输入监督学习决策树模型中,基于CART算法,使用Python (3.7.0)sklearn.tree模块DecisionTreeClassifier函数进行特征选择和剪枝;使用sklearn.model_selection模块cross_val_score函数进行五折交叉验证,并计算模型准确度;使用joblib模块进行模型持久化;使用graphviz模块绘制决策树模型,最终决策树模型包含13个最优的特征基因,13个最优特征基因包括9个负向预测基因(SMARCB1,TSC2,BAP1,SDHB,RIT1,ESR1, SOCS1,SH2B3,IDH2)和4个正向预测基因(MET,BRIP1, NTRK3,FGFR4);根据筛选的13个最优的特征基因,构建包含13个最优的特征基因的预测模型(式1),(式1),进行单因素cox回归分析(见图1)。结果显示,13个基因突变与整体生存存在显著相关性(p<0.05);
将TMB-H患者的样本进行靶向深度测序,检测panel基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后高风险和低风险组(见图2),结果显示接受免疫治疗后,TMB-H低风险组预后生存效果显著优于TMB-H高风险组与TMB-L组。
对比例
将9个负向基因、4个正向基因与13个综合基因分别构建模型对TMB-H人群进行划分,并比较人群接受免疫治疗后获益情况(DCR),如图3所示,结果表明,负向9基因、正向4基因与综合13基因划分的不同风险人群预后存在显著差异,并且综合13基因分类人群临床获益差异最为明显。
最后需要说明,上述描述仅为本发明的优选实施例,本领域的技术人员在本发明的启示下,在不违背本发明宗旨及权利要求的前提下,可以做出多种类似的表示,这样的变换均落入本发明的保护范围之内。
Claims (4)
1.一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于包括以下步骤:
S1. 对非小细胞肺癌患者进行基因靶向测序,获取基因变异情况;
S2. 将基因突变列表与预后情况输入监督学习决策树模型中,基于CART算法,建立分类模型,创建决策树模型,筛选出13个最优特征基因;
S3. 构建公式I所示的包含13个最优的特征基因的预测模型,用于对非小细胞肺癌TMB-H患者进行预后风险评分,从而预测出病人的预后情况;
其中,所述S2具体为:将检测到的基因变异做为训练特征,以总生存期OS<12 月以及总生存期OS>12月作为机器学习的分类结果,使用监督学习决策树模型CART算法,通过特征选择、剪枝、交叉验证、模型持久化四个步骤,创建决策树模型;所述特征选择具体为:使用公式II所示的基尼系数作为衡量标准,来计算通过不同的特征进行分支选择后的分类情况,找出最好的分类特征作为根节点,并以此类推,直到建树结束,创建复杂树模型;
其中K表示类别, p为第k个类别的概率,Gini(p)越小,则纯度越高,特征越好;
所述剪枝具体为:通过公式III所示的极小化决策树整体的损失函数来实现,对所述复杂树模型自下往上的对非叶子结点进行考回缩,若将该结点对应的子树替换为叶结点能带来泛化性能提升,则进行剪枝,即将父结点变为新的叶子结点,从而对将生成的树进行简化,以避免过拟合情况的发生;
其中,C(T)表示模型与训练数据的拟合程度,|T|表示模型复杂度;
所述交叉验证具体为:通过公式IV所示的准确度公式,将原始数据集随机地分成5份,每次将其中4份作为训练集来训练模型,剩余1份作为验证集验证模型,得到验证集的准确度,轮流进行5次,直到所有的数据都被验证了一次且仅被验证一次,循环计算每组模型准确度得分平均值,取平均值最高为最优模型,从而评价模型的泛化能力,从而进行模型及参数选择;
其中,Accuracy 代表准确率,TP:被正确分类的正例样本个数,TN:被正确分类的负例样本个数,FP:被错误分类的负例样本个数,FN:被错误分类的正例样本个数。
2.根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于所述S1具体为:采集非小细胞肺癌TMB-H患者样本进行靶向深度测序,检测基因变异情况。
3. 根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于:所述13个最优特征基因为SMARCB1、TSC2、 BAP1、SDHB、RIT1、ESR1、SOCS1、SH2B3、IDH2、MET、 BRIP1、NTRK3、FGFR4。
4.根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于:将TMB-H患者的样本进行靶向深度测序,检测panel基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后良好组与预后较差组。
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